To effectively capture preferences of humans for certain outcomes over certain others, we use insights from empirical models of human behavior from the social sciences.
These models have shown that humans derive utility relative to a reference point [1]}.
There is also a tendency to be more sensitive to losses than gains, and rather than using actual probabilities to assess outcomes, very small and very large probabilities are distorted [2]}.
As an illustration, human drivers on the road i) are more sensitive to changes in speed, than its absolute value; ii) are more averse to being passed (loss) than passing another car (gain); iii) overestimate small probabilities of engine failures and underestimate large probabilities of running out of gas.
Cumulative prospect theory (CPT), introduced in [3]}, is a framework that incorporates the above properties.
CPT uses a non-linear utility function to transform outcomes and a non-linear weighting function to distort probabilities in the cumulative distribution function.
Utility and weighting functions corresponding to gains and losses can be different to model the possibility that these are often interpreted in different ways by a human.
